class_docstr: Add a random flipping layer as pre-processing.
docstr: :param flip_axes: a list of indices over which to flip
  :param flip_probability: the probability of performing the flip
      (default = 0.5)
  :param name:
docstr_tok: [":","param","flip_axes",":","a","list","of","indices","over","which","to","flip",":","param","flip_probability",":","the","probability","of","performing","the","flip","(","default","=","0.5",")",":","param","name",":"]
code: def __init__(
      self, flip_axes, flip_probability=0.5, name="random_flip"
  ):
      """
  
      :param flip_axes: a list of indices over which to flip
      :param flip_probability: the probability of performing the flip
          (default = 0.5)
      :param name:
      """
      super(RandomFlipLayer, self).__init__(name=name)
      self._flip_axes = flip_axes
      self._flip_probability = flip_probability
      self._rand_flip = None
  
file_path: NiftyNet/niftynet/layer/rand_flip.py
imports: from __future__ import absolute_import, print_function
  import warnings
  import numpy
  from niftynet.layer.base_layer import RandomisedLayer
label_desc: Use image node_id 1 to create a new RandomFlipLayer object from inherited base classes: RandomisedLayer with example: obj = RandomFlipLayer(flip_axes, flip_probability, name)